Articles | Volume 20, issue 12
https://doi.org/10.5194/bg-20-2455-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-20-2455-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance
Alexander J. Norton
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA
A. Anthony Bloom
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA
Nicholas C. Parazoo
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA
Paul A. Levine
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA
Shuang Ma
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA
Renato K. Braghiere
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA
T. Luke Smallman
School of GeoSciences and NCEO, University of Edinburgh, Edinburgh EH8 9XP, UK
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Cited
13 citations as recorded by crossref.
- Decoupling of greenness and photosynthesis regulates phenological shifts across Australian ecosystems J. Xue et al. https://doi.org/10.1016/j.agrformet.2026.111253
- Spatiotemporal Variability of Vegetation Phenology and Its Response Mechanism to Extreme Climate Events in Southwest China Z. Liu et al. https://doi.org/10.1007/s11769-025-1592-4
- Global vegetation productivity has become less sensitive to drought in the first two decades of the 21st century M. Luo et al. https://doi.org/10.1016/j.jag.2024.104297
- Enhancing forest ecosystem simulation in the TASC model through the integration of the DAYCENT forest model S. Dangol et al. https://doi.org/10.1016/j.envsoft.2025.106565
- Deep learning meets tree phenology modelling: PhenoFormer versus process‐based models V. Garnot et al. https://doi.org/10.1111/2041-210X.70037
- Evaluation and Fusion of MODIS Data to Enhance the Reliability of GK2A/GK2B Geostationary Satellite Vegetation Indices J. Baik et al. https://doi.org/10.9798/KOSHAM.2025.25.6.39
- Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska D. Yang et al. https://doi.org/10.1088/2752-664X/ad9eb8
- Water Stress Dominates 21st‐Century Tropical Land Carbon Uptake P. Levine et al. https://doi.org/10.1029/2023GB007702
- Stability and transferability of broadly trained phenology models in a changing climate L. Spafford et al. https://doi.org/10.1016/j.agrformet.2025.110685
- The overestimated negative temperature sensitivity of the carbon sink in ecosystem models S. Zhu et al. https://doi.org/10.1007/s11707-025-1174-x
- Assessing Spring Phenology Models with Photosynthesis Integration: Mechanistic Drivers of the Carbon–Frost Trade-Off Y. Gu et al. https://doi.org/10.3390/f17020287
- Utility of Leaf Area Index for Monitoring Phenology of Russian Forests N. Shabanov et al. https://doi.org/10.3390/rs15225419
- The Coexistence of Trees, Shrubs, and Grasses Creates a Complex Picture of Land Surface Phenology in Dry Tropical Ecosystems S. Koolen et al. https://doi.org/10.3390/rs17162883
13 citations as recorded by crossref.
- Decoupling of greenness and photosynthesis regulates phenological shifts across Australian ecosystems J. Xue et al. https://doi.org/10.1016/j.agrformet.2026.111253
- Spatiotemporal Variability of Vegetation Phenology and Its Response Mechanism to Extreme Climate Events in Southwest China Z. Liu et al. https://doi.org/10.1007/s11769-025-1592-4
- Global vegetation productivity has become less sensitive to drought in the first two decades of the 21st century M. Luo et al. https://doi.org/10.1016/j.jag.2024.104297
- Enhancing forest ecosystem simulation in the TASC model through the integration of the DAYCENT forest model S. Dangol et al. https://doi.org/10.1016/j.envsoft.2025.106565
- Deep learning meets tree phenology modelling: PhenoFormer versus process‐based models V. Garnot et al. https://doi.org/10.1111/2041-210X.70037
- Evaluation and Fusion of MODIS Data to Enhance the Reliability of GK2A/GK2B Geostationary Satellite Vegetation Indices J. Baik et al. https://doi.org/10.9798/KOSHAM.2025.25.6.39
- Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska D. Yang et al. https://doi.org/10.1088/2752-664X/ad9eb8
- Water Stress Dominates 21st‐Century Tropical Land Carbon Uptake P. Levine et al. https://doi.org/10.1029/2023GB007702
- Stability and transferability of broadly trained phenology models in a changing climate L. Spafford et al. https://doi.org/10.1016/j.agrformet.2025.110685
- The overestimated negative temperature sensitivity of the carbon sink in ecosystem models S. Zhu et al. https://doi.org/10.1007/s11707-025-1174-x
- Assessing Spring Phenology Models with Photosynthesis Integration: Mechanistic Drivers of the Carbon–Frost Trade-Off Y. Gu et al. https://doi.org/10.3390/f17020287
- Utility of Leaf Area Index for Monitoring Phenology of Russian Forests N. Shabanov et al. https://doi.org/10.3390/rs15225419
- The Coexistence of Trees, Shrubs, and Grasses Creates a Complex Picture of Land Surface Phenology in Dry Tropical Ecosystems S. Koolen et al. https://doi.org/10.3390/rs17162883
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
This study explores how the representation of leaf phenology affects our ability to predict changes to the carbon balance of land ecosystems. We calibrate a new leaf phenology model against a diverse range of observations at six forest sites, showing that it improves the predictive capability of the processes underlying the ecosystem carbon balance. We then show how changes in temperature and rainfall affect the ecosystem carbon balance with this new model.
This study explores how the representation of leaf phenology affects our ability to predict...
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